JMLR
Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python
Authors
Caglar Demir
Alkid Baci
N'Dah Jean Kouagou
Leonie Nora Sieger
Stefan Heindorf
Simon Bin
Lukas Blübaum
Alexander Bigerl
Axel-Cyrille Ngonga Ngomo
Paper Information
-
Journal:
Journal of Machine Learning Research -
Added to Tracker:
Jul 15, 2025
Abstract
In this paper, we present Ontolearn---a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent state-of-the-art symbolic and neuro-symbolic class expression learners including EvoLearner and DRILL. A learned OWL class expression can be used to classify instances in the knowledge graph. Furthermore, Ontolearn integrates a verbalization module based on an LLM to translate complex OWL class expressions into natural language sentences. By mapping OWL class expressions into respective SPARQL queries, Ontolearn can be easily used to operate over a remote triplestore. The source code of Ontolearn is available at https://github.com/dice-group/Ontolearn.
Author Details
Caglar Demir
AuthorAlkid Baci
AuthorN'Dah Jean Kouagou
AuthorLeonie Nora Sieger
AuthorStefan Heindorf
AuthorSimon Bin
AuthorLukas Blübaum
AuthorAlexander Bigerl
AuthorAxel-Cyrille Ngonga Ngomo
AuthorCitation Information
APA Format
Caglar Demir
,
Alkid Baci
,
N'Dah Jean Kouagou
,
Leonie Nora Sieger
,
Stefan Heindorf
,
Simon Bin
,
Lukas Blübaum
,
Alexander Bigerl
&
Axel-Cyrille Ngonga Ngomo
.
Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python.
Journal of Machine Learning Research
.
BibTeX Format
@article{JMLR:v26:24-1113,
author = {Caglar Demir and Alkid Baci and N'Dah Jean Kouagou and Leonie Nora Sieger and Stefan Heindorf and Simon Bin and Lukas Bl{{\"u}}baum and Alexander Bigerl and Axel-Cyrille Ngonga Ngomo},
title = {Ontolearn---A Framework for Large-scale OWL Class Expression Learning in Python},
journal = {Journal of Machine Learning Research},
year = {2025},
volume = {26},
number = {63},
pages = {1--6},
url = {http://jmlr.org/papers/v26/24-1113.html}
}